! git log -1 --format="%H"

28688145c190ebc1427f3830d5580e6337c2ebbf

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 2
# The number of cities in the core net
NC = 3
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 7667, C = 5292

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 1640 | is_connectable
   82 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  123 | is_connected_step
 3362 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 1200.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 1200.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 5292 rows, 7667 columns and 25297 nonzeros
Model fingerprint: 0x02f91814
Model has 3 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 5207 integer (5207 binary)
Coefficient statistics:
  Matrix range     [1e+00, 3e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 87 rows and 1768 columns
Presolve time: 0.08s
Presolved: 5205 rows, 5899 columns, 25555 nonzeros
Variable types: 2460 continuous, 3439 integer (3439 binary)
Found heuristic solution: objective 117286.47478

Root relaxation: objective 1.155515e+03, 5397 iterations, 0.12 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 1155.51469    0  145 117286.475 1155.51469  99.0%     -    0s
H    0     0                    73614.728610 1155.51469  98.4%     -    0s
     0     0 1720.13230    0  209 73614.7286 1720.13230  97.7%     -    0s
H    0     0                    44583.046276 1720.13230  96.1%     -    0s
     0     0 2516.15753    0  209 44583.0463 2516.15753  94.4%     -    0s
     0     0 2516.15753    0  209 44583.0463 2516.15753  94.4%     -    0s
H    0     0                    31718.611787 2516.15753  92.1%     -    0s
     0     0 2572.57343    0  264 31718.6118 2572.57343  91.9%     -    0s
     0     0 2579.21254    0  281 31718.6118 2579.21254  91.9%     -    0s
     0     0 2579.27473    0  306 31718.6118 2579.27473  91.9%     -    0s
     0     0 2590.18035    0  292 31718.6118 2590.18035  91.8%     -    1s
     0     0 2592.29831    0  270 31718.6118 2592.29831  91.8%     -    1s
     0     0 2592.34915    0  270 31718.6118 2592.34915  91.8%     -    1s
     0     0 2596.81952    0  264 31718.6118 2596.81952  91.8%     -    1s
     0     0 2607.26047    0  255 31718.6118 2607.26047  91.8%     -    1s
     0     0 2607.70788    0  259 31718.6118 2607.70788  91.8%     -    1s
     0     0 2613.70029    0  292 31718.6118 2613.70029  91.8%     -    1s
     0     0 2614.43503    0  270 31718.6118 2614.43503  91.8%     -    1s
H    0     0                    24701.468954 2664.65079  89.2%     -    1s
     0     0 2673.22987    0  307 24701.4690 2673.22987  89.2%     -    1s
     0     0 2673.22987    0  249 24701.4690 2673.22987  89.2%     -    1s
H    0     0                    19540.340247 2673.22987  86.3%     -    2s
     0     2 2673.22987    0  245 19540.3402 2673.22987  86.3%     -    2s
H   30    24                    19107.793139 2821.51474  85.2%   556    3s
H   70    62                    18874.907520 2821.51474  85.1%   322    4s
H   75    62                    18762.580732 2821.51474  85.0%   305    4s
H  111    93                    18076.090246 2821.51474  84.4%   243    4s
H  114    93                    18060.227940 2821.51474  84.4%   251    4s
   119   103 3771.53984   16  243 18060.2279 2821.51474  84.4%   252    5s
H  157   125                    17991.191179 2821.51474  84.3%   224    5s
H  157   125                    17937.725927 2821.51474  84.3%   224    5s
H  162   125                    16112.039754 2821.51474  82.5%   217    5s
H  233   174                    16068.509142 2821.51474  82.4%   206    6s
H  245   191                    16063.139145 2821.51474  82.4%   215    6s
  1295  1013 6354.56906    6  184 16063.1391 4572.09421  71.5%   131   10s
  1503  1102 4572.09421   13  234 16063.1391 4572.09421  71.5%   135   16s
  1557  1117 4572.09421   20  244 16063.1391 4572.09421  71.5%   148   20s
H 1629  1040                    15378.840478 4572.09421  70.3%   152   23s
H 1669   977                    15326.445943 4572.09421  70.2%   154   23s
H 1710   909                    15316.445943 4572.09421  70.1%   155   24s
H 1712   861                    15308.978130 4572.09421  70.1%   155   24s
  1791   830     cutoff   27      15308.9781 4572.09421  70.1%   155   25s
  2541   777 7229.17502   31  198 15308.9781 4572.09421  70.1%   145   30s
  2960   771 12106.2027   25  177 15308.9781 7156.64696  53.3%   148   35s
  4382  1088 10481.2223   45  122 15308.9781 8347.62386  45.5%   134   40s
H 4782  1285                    15195.189396 8677.69538  42.9%   131   41s
H 4924  1277                    15077.913727 8776.76152  41.8%   131   41s
  6118  1681 10958.6656   47  129 15077.9137 9342.64390  38.0%   126   45s
  8556  1498     cutoff   60      15077.9137 10841.9427  28.1%   122   50s
H10306   691                    15077.913718 12126.4288  19.6%   119   54s
 11231   103 infeasible   51      15077.9137 13710.2986  9.07%   114   55s

Cutting planes:
  Gomory: 36
  Cover: 89
  Implied bound: 10
  Clique: 1
  MIR: 79
  Flow cover: 100
  GUB cover: 4
  Inf proof: 1
  Zero half: 22
  RLT: 55
  Relax-and-lift: 11

Explored 16104 nodes (1322910 simplex iterations) in 57.41 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 15077.9 15077.9 15195.2 ... 16112

Optimal solution found (tolerance 1.00e-04)
Best objective 1.507791371771e+04, best bound 1.507791371771e+04, gap 0.0000%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	2.3	2.800000e+00	5.1
1	1	Borås	False	False	1.9	2.520000e+01	27.1
2	2	Eskilstuna	False	False	1.3	3.900000e+00	5.2
3	3	Falun	False	False	1.9	1.278977e-13	1.9
4	4	Gävle	False	False	0.7	2.610000e+01	26.8
5	5	Göteborg	False	False	6.4	2.273737e-13	6.4
6	6	Halmstad	False	False	3.4	1.160000e+01	15.0
7	7	Haparanda	False	False	3.9	0.000000e+00	3.9
8	8	Helsingborg	False	False	3.7	7.900000e+00	11.6
9	9	Hudiksvall	False	False	1.3	2.230000e+01	23.6
10	10	Jönköping	True	False	2.9	6.530000e+01	68.2
11	11	Kalmar	False	False	2.8	3.552714e-13	2.8
12	12	Karlskrona	False	False	4.3	5.115908e-13	4.3
13	13	Karlstad	False	False	2.4	9.308110e-13	2.4
14	14	Kiruna	False	False	2.8	1.705303e-13	2.8
15	15	Kristianstad	False	False	4.5	6.785683e-13	4.5
16	16	Lidköping	False	False	0.7	8.100000e+00	8.8
17	17	Linköping	True	False	2.9	1.221000e+02	125.0
18	18	Luleå	False	False	4.4	9.000000e+00	13.4
19	19	Malmö	False	False	4.2	3.700000e+00	7.9
20	20	Motala	True	True	3.1	1.315000e+02	9.6
21	21	Norrköping	False	False	3.4	4.710000e+01	50.5
22	22	Nyköping	False	False	4.4	3.750000e+01	41.9
23	23	Sandviken	False	False	2.5	1.200817e-12	2.5
24	24	Skellefteå	False	False	1.4	1.340000e+01	14.8
25	25	Skövde	False	False	3.1	8.800000e+00	11.9
26	26	Stockholm	False	False	7.7	2.980000e+01	37.5
27	27	Sundsvall	False	False	2.1	2.020000e+01	22.3
28	28	Trelleborg	False	False	3.7	1.566747e-12	3.7
29	29	Uddevalla	False	False	4.0	2.273737e-12	4.0
30	30	Umeå	False	False	1.9	1.480000e+01	16.7
31	31	Uppsala	False	False	3.0	2.680000e+01	29.8
32	32	Varberg	False	False	3.8	1.500000e+01	18.8
33	33	Vetlanda	False	False	0.8	1.590000e+01	16.7
34	34	Vänersborg	False	False	4.1	4.000000e+00	8.1
35	35	Västervik	False	False	3.4	2.486900e-12	3.4
36	36	Västerås	False	False	3.9	2.870593e-12	3.9
37	37	Växjö	False	False	4.3	8.800000e+00	13.1
38	38	Örebro	False	False	2.2	4.300000e+00	6.5
39	39	Örnsköldsvik	False	False	2.5	1.670000e+01	19.2
40	40	Östersund	False	False	1.0	9.201528e-13	1.0

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	14.8	557.326132	111
1	Eskilstuna	Västerås	SUB	-3.9	44.775012	43
2	Gävle	Hudiksvall	SUB	-23.6	966.610215	118
3	Norrköping	Nyköping	SUB	-41.9	595.269663	58
4	Nyköping	Stockholm	SUB	-37.5	1022.500000	90
5	Karlskrona	Växjö	SUB	4.3	150.077607	102
6	Linköping	Norrköping	SUB	-50.5	476.090652	44
7	Motala	Örebro	SUB	-6.5	191.382381	92
8	Boden	Kiruna	SUB	-2.8	449.539100	291
9	Sundsvall	Östersund	SUB	-1.0	77.633542	166
10	Halmstad	Helsingborg	SUB	-11.6	267.571986	79
11	Jönköping	Vetlanda	SUB	-16.7	286.749284	65
12	Umeå	Örnsköldsvik	SUB	16.7	627.590973	111
13	Boden	Luleå	SUB	5.1	39.194104	32
14	Lidköping	Skövde	SUB	8.8	105.450189	49
15	Karlstad	Örebro	SUB	2.4	61.279919	77
16	Halmstad	Varberg	SUB	15.0	258.577201	65
17	Lidköping	Vänersborg	SUB	-8.1	129.045201	60
18	Uddevalla	Vänersborg	SUB	4.0	22.172747	21
19	Gävle	Sandviken	SUB	-2.5	19.882118	25
20	Linköping	Motala	CORE	125.0	510.000000	51
21	Borås	Göteborg	SUB	-6.4	131.078630	71
22	Jönköping	Linköping	CORE	68.2	1250.000000	125
23	Linköping	Västervik	SUB	-3.4	112.715626	97
24	Sundsvall	Örnsköldsvik	SUB	-19.2	878.974055	127
25	Jönköping	Motala	CORE	-9.6	1080.000000	108
26	Vetlanda	Växjö	SUB	-13.1	263.087118	72
27	Borås	Varberg	SUB	-18.8	467.695643	84
28	Haparanda	Luleå	SUB	3.9	180.293133	124
29	Luleå	Skellefteå	SUB	13.4	659.953460	133
30	Stockholm	Uppsala	SUB	-29.8	550.119554	69
31	Kristianstad	Växjö	SUB	4.5	197.061487	120
32	Gävle	Uppsala	SUB	26.8	403.877951	60
33	Helsingborg	Malmö	SUB	-7.9	126.105814	60
34	Falun	Örebro	SUB	1.9	125.944766	155
35	Borås	Jönköping	SUB	27.1	646.341239	82
36	Jönköping	Skövde	SUB	-11.9	284.330749	81
37	Hudiksvall	Sundsvall	SUB	-22.3	524.081992	81
38	Malmö	Trelleborg	SUB	-3.7	33.196374	34
39	Eskilstuna	Norrköping	SUB	5.2	179.396176	102
40	Kalmar	Vetlanda	SUB	2.8	124.941927	119

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  2840.000
subnet = 12237.914
------------------
total  = 15077.914

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))